ISSN 2394-5125

    Diabetes Prediction using Machine Learning Techniques (2023)

    Gorisetty Nirosha ,Dr. G.Sharmila Sujatha
    JCR. 2023: 36-42


    Diabetes is a chronic disease with the potential to cause a worldwide health care crisis. According to International Diabetes Federation 382 million people are living with diabetes across the whole world. By 2035, this will be doubled as 592 million. Diabetes is a disease caused due to the increase level of blood glucose. This high blood glucose produces the symptoms of frequent urination, increased thirst, and increased hunger. Diabetes is a one of the leading cause of blindness, kidney failure, amputations, heart failure and stroke. When we eat our body turns food into sugars, or glucose. At the point, one pancreas is supposed to release insulin. Insulin serves as a key to open our cells, to allow the glucose to enter and allow us to use the glucose for energy. But with diabetes, this system does not work. Type 1 and Type 2 diabetes are the most common forms of the disease, but there are also other kinds, such as gestational diabetes, which occurs during pregnancy, as well as other forms. Machine learning is an emerging scientific field in data science dealing with the ways in which machines learn from experience. The aim of this project is to develop a system which can perform early prediction of diabetes for a patient with a higher accuracy by combining the results of different machine learning techniques. To achieve this goal this project work we will do early prediction of Diabetes in a human body or a patient for a higher accuracy through applying, various Machine Learning Techniques. Machine constructing models from datasets collected from patients. In this work we will use Machine Learning Classification and ensemble techniques on a dataset to predict diabetes. Which are Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), GaussianNB (GNB). The accuracy is different for every model when compared to other models. The Project work gives the accurate of higher accuracy model shows that the model is capable of predicting diabetes effectively.


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    Volume & Issue

    Volume 10 Issue-4